Mental representations of objects reflect the ways in which we interact
with them
- URL: http://arxiv.org/abs/2007.04245v2
- Date: Tue, 11 May 2021 13:06:41 GMT
- Title: Mental representations of objects reflect the ways in which we interact
with them
- Authors: Ka Chun Lam, Francisco Pereira, Maryam Vaziri-Pashkam, Kristin
Woodard, Emalie McMahon
- Abstract summary: We introduce a method to represent objects in a space where each dimension corresponds to a broad mode of interaction.
We show that the dimensions in this space can be used to predict categorical and functional dimensions in a state-of-the-art mental representation of objects.
- Score: 1.0207955314209531
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In order to interact with objects in our environment, humans rely on an
understanding of the actions that can be performed on them, as well as their
properties. When considering concrete motor actions, this knowledge has been
called the object affordance. Can this notion be generalized to any type of
interaction that one can have with an object? In this paper we introduce a
method to represent objects in a space where each dimension corresponds to a
broad mode of interaction, based on verb selectional preferences in text
corpora. This object embedding makes it possible to predict human judgments of
verb applicability to objects better than a variety of alternative approaches.
Furthermore, we show that the dimensions in this space can be used to predict
categorical and functional dimensions in a state-of-the-art mental
representation of objects, derived solely from human judgements of object
similarity. These results suggest that interaction knowledge accounts for a
large part of mental representations of objects.
Related papers
- Which objects help me to act effectively? Reasoning about physically-grounded affordances [0.6291443816903801]
A key aspect of this understanding lies in detecting an object's affordances.
Our approach leverages a dialogue of large language models (LLMs) and vision-language models (VLMs) to achieve open-world affordance detection.
By grounding our system in the physical world, we account for the robot's embodiment and the intrinsic properties of the objects it encounters.
arXiv Detail & Related papers (2024-07-18T11:08:57Z) - LEMON: Learning 3D Human-Object Interaction Relation from 2D Images [56.6123961391372]
Learning 3D human-object interaction relation is pivotal to embodied AI and interaction modeling.
Most existing methods approach the goal by learning to predict isolated interaction elements.
We present LEMON, a unified model that mines interaction intentions of the counterparts and employs curvatures to guide the extraction of geometric correlations.
arXiv Detail & Related papers (2023-12-14T14:10:57Z) - Modelling Spatio-Temporal Interactions for Compositional Action
Recognition [21.8767024220287]
Humans have the natural ability to recognize actions even if the objects involved in the action or the background are changed.
We show the effectiveness of our interaction-centric approach on the compositional Something-Else dataset.
Our approach of explicit human-object-stuff interaction modeling is effective even for standard action recognition datasets.
arXiv Detail & Related papers (2023-05-04T09:37:45Z) - Fine-grained Affordance Annotation for Egocentric Hand-Object
Interaction Videos [27.90643693526274]
Object affordance provides information on action possibilities based on human motor capacity and objects' physical property.
This paper proposes an efficient annotation scheme to address these issues by combining goal-irrelevant motor actions and grasp types as affordance labels.
We provide new annotations by applying this scheme to the EPIC-KITCHENS dataset and test our annotation with tasks such as affordance recognition, hand-object interaction hotspots prediction, and cross-domain evaluation of affordance.
arXiv Detail & Related papers (2023-02-07T07:05:00Z) - Full-Body Articulated Human-Object Interaction [61.01135739641217]
CHAIRS is a large-scale motion-captured f-AHOI dataset consisting of 16.2 hours of versatile interactions.
CHAIRS provides 3D meshes of both humans and articulated objects during the entire interactive process.
By learning the geometrical relationships in HOI, we devise the very first model that leverage human pose estimation.
arXiv Detail & Related papers (2022-12-20T19:50:54Z) - Learn to Predict How Humans Manipulate Large-sized Objects from
Interactive Motions [82.90906153293585]
We propose a graph neural network, HO-GCN, to fuse motion data and dynamic descriptors for the prediction task.
We show the proposed network that consumes dynamic descriptors can achieve state-of-the-art prediction results and help the network better generalize to unseen objects.
arXiv Detail & Related papers (2022-06-25T09:55:39Z) - Bi-directional Object-context Prioritization Learning for Saliency
Ranking [60.62461793691836]
Existing approaches focus on learning either object-object or object-scene relations.
We observe that spatial attention works concurrently with object-based attention in the human visual recognition system.
We propose a novel bi-directional method to unify spatial attention and object-based attention for saliency ranking.
arXiv Detail & Related papers (2022-03-17T16:16:03Z) - Understanding Synonymous Referring Expressions via Contrastive Features [105.36814858748285]
We develop an end-to-end trainable framework to learn contrastive features on the image and object instance levels.
We conduct extensive experiments to evaluate the proposed algorithm on several benchmark datasets.
arXiv Detail & Related papers (2021-04-20T17:56:24Z) - Object Properties Inferring from and Transfer for Human Interaction
Motions [51.896592493436984]
In this paper, we present a fine-grained action recognition method that learns to infer object properties from human interaction motion alone.
We collect a large number of videos and 3D skeletal motions of the performing actors using an inertial motion capture device.
In particular, we learn to identify the interacting object, by estimating its weight, or its fragility or delicacy.
arXiv Detail & Related papers (2020-08-20T14:36:34Z) - Human and Machine Action Prediction Independent of Object Information [1.0806206850043696]
We study the role of inter-object relations that change during an action.
We predict actions in, on average, less than 64% of the action's duration.
arXiv Detail & Related papers (2020-04-22T12:13:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.